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Apache Spark Training Courses

Local, instructor-led live Apache Spark training courses demonstrate through hands-on practice how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis.

Apache Spark training is available as "onsite live training" or "remote live training". Onsite live Apache Spark training can be carried out locally on customer premises in the Philippines or in NobleProg corporate training centers in the Philippines. Remote live training is carried out by way of an interactive, remote desktop.

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Testimonials

★★★★★

★★★★★

Richard is very calm and methodical, with an analytic insight - exactly the qualities needed to present this sort of course.

Kieran Mac Kenna

Course: Spark for Developers

We know a lot more about the whole environment.

John Kidd

Course: Spark for Developers

The trainer made the class interesting and entertaining which helps quite a bit with all day training.

Ryan Speelman

Course: Spark for Developers

I think the trainer had an excellent style of combining humor and real life stories to make the subjects at hand very approachable. I would highly recommend this professor in the future.

Course: Spark for Developers

Ernesto did a great job explaining the high level concepts of using Spark and its various modules.

Michael Nemerouf

Course: Spark for Developers

This is one of the best hands-on with exercises programming courses I have ever taken.

Spark Subcategories

Apache Spark Course Outlines

This course will introduce Apache Spark. The students will learn how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis. The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX.

MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

It divides into two packages:

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spark.mllib contains the original API built on top of RDDs.

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spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.

Audience

This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark

Hortonworks Data Platform is an open-source Apache Hadoop support platform that provides a stable foundation for developing big data solutions on the Apache Hadoop ecosystem.

This instructor-led live training introduces Hortonworks and walks participants through the deployment of Spark + Hadoop solution.

By the end of this training, participants will be able to:

- Use Hortonworks to reliably run Hadoop at a large scale- Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.- Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project- Process different types of data, including structured, unstructured, in-motion, and at-rest.

Audience

- Hadoop administrators

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics.

This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark.

Alluxio is an open-source virtual distributed storage system that unifies disparate storage systems and enables applications to interact with data at memory speed. It is used by companies such as Intel, Baidu and Alibaba.

In this instructor-led, live training, participants will learn how to use Alluxio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio.

Many real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and processes -- these tools and processes can be referred to as Graph Computing (also known as Graph Analytics).

In this instructor-led, live training, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.

By the end of this training, participants will be able to:

- Understand how graph data is persisted and traversed.- Select the best framework for a given task (from graph databases to batch processing frameworks.)- Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.- View real-world big data problems in terms of graphs, processes and traversals.

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python.

In this instructor-led, live training, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.

By the end of this training, participants will be able to:

- Learn how to use Spark with Python to analyze Big Data.- Work on exercises that mimic real world circumstances.- Use different tools and techniques for big data analysis using PySpark.

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Spark SQL is Apache Spark's module for working with structured and unstructured data. Spark SQL provides information about the structure of the data as well as the computation being performed. This information can be used to perform optimizations. Two common uses for Spark SQL are:- to execute SQL queries.- to read data from an existing Hive installation.

In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL.

Stream Processing refers to the real-time processing of "data in motion", that is, performing computations on data as it is being received. Such data is read as continuous streams from data sources such as sensor events, website user activity, financial trades, credit card swipes, click streams, etc. Stream Processing frameworks are able to read large volumes of incoming data and provide valuable insights almost instantaneously.

In this instructor-led, live training (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.

By the end of this training, participants will be able to:

- Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming- Understand and select the most appropriate framework for the job- Process of data continuously, concurrently, and in a record-by-record fashion- Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.- Integrating the most appropriate stream processing library with enterprise applications and microservices

Audience

- Developers- Software architects

Format of the Course

- Part lecture, part discussion, exercises and heavy hands-on practice

Notes

- To request a customized training for this course, please contact us to arrange.

Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights.

The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment.

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises.

Apache Spark's learning curve is slowly increasing at the begining, it needs a lot of effort to get the first return. This course aims to jump through the first tough part. After taking this course the participants will understand the basics of Apache Spark , they will clearly differentiate RDD from DataFrame, they will learn Python and Scala API, they will understand executors and tasks, etc. Also following the best practices, this course strongly focuses on cloud deployment, Databricks and AWS. The students will also understand the differences between AWS EMR and AWS Glue, one of the lastest Spark service of AWS.

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